Fabrication and characterization of albumin‐acacia nanoparticles based on complex coacervation as potent nanocarrier

BACKGROUND: Bovine serum albumin (BSA) and Gum Arabic (Acacia) were used to fabricate nanoparticles (NPs) as a new nanocarrier for drug and bioactive delivery systems. Several parameters, namely pH, protein to polysaccharide ratio, ionic strength, total biopolymer concentration, temperature, agitation speed and glutaraldehyde concentration were investigated for their effect on the product preparation process. Besides fabrication, the stabilization step including heating, ultrasonicating, cross‐linking, and its effect on average size and polydispersity of NPs was examined. RESULTS: Total biopolymer concentration had no marked effect on the average size of prepared NPs although it did affect other systems produced by complex coacervation. The most effective parameters were pH, temperature and ionic strength, which affected this system (albumin‐acacia) by a complicated route. The smallest size of prepared NPs (i.e. the best product) was 108 nm, which was fabricated under the optimum condition of T = 4 °C, pH = 4.9, $Pr{:}Ps = 2$ , and Is = 0. CONCLUSION: Of the stabilization methods tested, heating gave the best results. The fabricated NPs were purified with a 0.45 µm syringe filter and analyzed by PCS, SEM, FTIR and DSC. The mechanism of complex coacervation for preparing NPs, and their use as a nanodelivery system are discussed. Copyright © 2012 Society of Chemical Industry     

The reuse of waste cooking oil and spent bleaching earth to produce biodiesel

Currently, semi-refined and refined vegetable oils are used as a feedstock in biodiesel production. However, criteria such as competition with conventional fossil fuel, economic reasons, shortage supply of food and its social impact on the global scale have somewhat slowed the development of the biodiesel industry. Spent bleaching earth is currently under-utilized by deposition in landfills with no attempt to recover the oil. In this study the waste oil adsorbed on spent bleaching earth, refined soybean oil, and waste cooking oil were evaluated as potential sources of biodiesel production in Iran. Different characteristics of the oil samples, such as fatty acid composition, peroxide, iodine, acid values, etc., were evaluated. A two-step esterification reaction using methanol was conducted to produce biofuel. Subsequently, physicochemical properties of produced biodiesel were analyzed. The oil content in spent bleaching earth was 19.3%, which was lowered to 3.7% using hexane as the solvent. Gas chromatography showed that palmitic, oleic, and linoleic acids were predominantly fatty acids, respectively, and the highest content of saturated acids belonged to waste cooking oil (24%). The acidity of 8.3% was obtained for the oil recovered from spent bleaching earth followed by waste cooking oil (3.6%) and refined soybean oil (0.1%). Totally, the specifications of all biodiesel produced were in the range defined by ASTM D6751 and EN 14214 standards. Since about 2000–3000 tones of spent bleaching earth residual oil is annually dumped and the amount of waste cooking oil produced yearly is 500,000 tones, there is a great potential for Iran to produce biodiesel from waste oils.     

Comparison between blended mode and fumigation mode on combustion,performance and emissions of a diesel engine fueled with ternary fuel (diesel-biodiesel-ethanol) based on engine speed

According to the literature, there is in lack of a comprehensive study to compare the combustion, performance and emissions of a diesel engine using diesel, biodiesel and ethanol fuels (DBE) in the blended mode and fumigation mode under various engine speeds. This study was conducted to fill this knowledge gap by comparing the effect of blended, fumigation and combined fumigation + blended (F + B) modes on the combustion, performance and emissions of a diesel engine under a constant engine load (50% of full torque) with five engine speeds ranging from 1400 rpm to 2200 rpm. A constant overall fuel composition of 80% diesel, 5% biodiesel and 15% ethanol, by volume % (D80B5E15), was utilized to provide the same fuel for comparing the three fueling modes.According to the average results of five engine speeds, the blended mode has higher peak heat release rate (HRR), ignition delay (ID), brake thermal efficiency (BTE), brake specific nitrogen monoxide (BSNO) and brake specific nitrogen oxides (BSNO_X), but lower duration of combustion (DOC), brake specific fuel consumption (BSFC), brake specific carbon dioxide (BSCO₂), brake specific carbon monoxide (BSCO), brake specific hydrocarbon (BSHC), brake specific nitrogen dioxide (BSNO₂), brake specific particulate matter (BSPM), total number concentration (TNC) and geometric mean diameter (GMD), and similar peak in-cylinder pressure compared to the fumigation mode. In addition, for almost all the parameters, results obtained in the F + B mode are in between those of the blended and fumigation modes. In regard to the effect of engine speed, the results reveal that the increase in engine speed causes reduction in peak in-cylinder pressure, BTE, BSHC, BSNO_X, BSNO and BSNO₂, but increase in peak HRR, ID, DOC, BSFC, BSCO₂, BSPM and TNC, and similar BSCO and GMD for almost all the tested fueling modes. It can be inferred that the blended mode is the suitable fueling mode, compared with the fumigation mode, under the operating conditions investigated in this study.     

Strengthening of polysulfone membranes using hybrid mixtures of micro- and nano-scale modifiers

Polysulfone (PSf) membranes were modified by either a new organic modifier (sulfonated poly(ether sulfide sulfone), SPESS) or a traditional modifier (rice hulk). These membranes were further reinforced with either multi-walled carbon nanotubes (MWCNTs) or silica nanoparticles. Having a dye rejection of 98.46%, the reinforced membranes increased more than 50% in strength but no change in solution flux was observed. The morphological and roughness studies were conducted using scanning electron microscopy and atomic force microscopy. Moreover, the PSF membranes were also characterized by differential scanning calorimetry. Modifying the membranes with organic modifier or nanofiller increases the glass transition temperature (T _g ). The highest T _g and strength were observed for the PSf-SPESSMWCNT membrane. SPESS decreases surface roughness but MWCNT increases roughness on the nanoscale. All membranes show a bimodal pore size distribution, whereas the PSf-SPESS-MWCNT membrane exhibits a relatively uniform distribution of macroscopic and microscopic pores.

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A mathematical formulation for joint channel assignment and multicast routing in multi-channel multi-radio wireless mesh networks

Multicast routing is generally an efficient mechanism for delivering identical content to a group of receivers. Multicast is also deemed a key enabling service for a wealth of audio and video applications as well as data dissemination protocols over the last-mile backhaul Internet connectivity provided by multi-channel multi-radio wireless mesh networks (MCMR WMNs). Major prior art multicast protocols in these networks center around heuristic or meta-heuristic initiatives in which channel assignment and multicast routing are considered as two separate sub-problems to be solved in sequence. It might even be the cast that the solution for either of these two sub-problems is assumed to be preparatively calculated and given as input to the other. Within this perspective, however, the interplay between the two sub-problems would essentially be ruled out from the computations, resulting in sub-optimal solutions for network configuration. The work in this article is targeted at promoting the adoption of cross-layer design for joint channel assignment and multicast tree construction problem in MCMR WMNs. In the proposed scheme, contrary to the existing methods, these two sub-problems will be solved conjointly and an optimal solution is provided. In particular, a comprehensive cross-optimization framework based on the binary integer programming (BIP) formulation of the problem is presented which also addresses the hidden channel problem in MCMR WMNs. We have, as well, conducted an extensive series of simulation experiments to verify the efficacy of the proposed method. Also, experimental results demonstrate that the proposed method outperforms the genetic algorithm and the simulated annealing based methods proposed by Cheng and Yang (2011) in terms of interference.     

Indexing in-network trajectory flows

Indexing moving objects (MO) is a hot topic in the field of moving objects databases since many years. An impressive number of access methods have been proposed to optimize the processing of MO-related queries. Several methods have focused on spatio-temporal range queries, which represent the foundation of MO trajectory queries. Surprisingly, only a few of them consider that the objects movements are constrained. This is an important aspect for several reasons ranging from better capturing the relationship between the trajectory and the network space to more accurate trajectory representation with lower storage requirements. In this paper, we propose T-PARINET, an access method to efficiently retrieve the trajectories of objects moving in networks. T-PARINET is designed for continuous indexing of trajectory data flows. The cornerstone of T-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B⁺-tree local indexes. Because the network can be modeled using graphs, the partitioning of the trajectory data makes use of graph partitioning theory and can be tuned for a given query load and a given data distribution in the network space. The tuning process is built on a good quality cost model that is supplied with PARINET. The advantage of having a cost model is twofold; it allows a better integration of the index into the query optimizer of any DBMS, and it permits tuning the index structure for better performance. The tuning process can be performed before the index creation in the case of historical data or online in the case of indexing data flows. In fact, massive online updates can degrade the index quality, which can be measured by the cost model. We propose a specific maintenance process that results into T-PARINET. We study different types of queries and provide an optimized configuration for several scenarios. T-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS shows that our method is robust. It also significantly outperforms the reference R-tree-based access methods for in-network trajectory databases.     

Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches

This study proposes a comprehensive data processing and modeling framework for building high‐accuracy machine learning model to predict the steam consumption of a gas sweetening process. The data pipeline processes raw historical data of this application and identifies the minimum number of modeling variables required for this prediction in order to ease the applicability and practicality of such methods in the industrial units. On the modeling end, an empirical comparison of most of the state‐of‐the‐arts regression algorithms was run in order to find the best fit to this specific case study. The ultimate goal is to leverage this model to identify the achievable energy conservation opportunity in such plants. The historical data for this modeling was collected from a gas treating plant at South Pars Gas Complex for 3 years from 2017 to 2019. This data gets passed through a multistage data processing scheme that conducts multicollinearity analysis and model‐based feature selection. For model selection, a wide range of regression algorithms from different classes of regressor have been considered. Among all these methods, the Gradient Boosting Machines model outperformed the others and achieved the lowest cross‐validation error. The results show that this model can predict the steam consumption values with 98% R‐squared accuracy on the holdout test set. Furthermore, the offline analysis demonstrates that there is a potential of 2% energy saving, equivalent to 24 000 metric tons of annual steam consumption reduction, which can be achieved by mapping the underperforming energy consumption states of the unit to the expected performances predicted by the model.     

Vision-based quantitative assessment of microcracks on reactor internal components of nuclear power plants

Ageing power facilities are increasingly susceptible to the onset of damage related to long exposure to stress, radiation, elevated temperatures and environmental conditions. One failure mechanism of particular concern is the onset of stress corrosion cracking. Currently, a technician manually measures the crack thicknesses at few points along a microcrack in a microscopic image, and the results are quantified by the Root Mean Square (RMS) of these measurements. This approach is time-consuming and subjective. In addition, the crack thickness is an important but difficult characteristic to accurately describe in a single numerical value, since it can vary considerably along the length of a crack. In this paper, a vision-based methodology is proposed for accurate quantification of microcracks that provides the thickness measurements for each pixel along the crack centreline. The proposed approach provides the minimum, maximum, mean, standard deviation, RMS and the histogram of crack thicknesses, which in most cases yields thousands of measurements. This approach provides more comprehensive insight regarding the condition of a microcrack. A region-growing method is used for segmenting microcracks from complex backgrounds. The microcrack thicknesses are then automatically computed along the lines orthogonal to the crack centreline. The fast marching method is used to accurately estimate the centreline of microcracks. Several real microcracks are examined to evaluate the performance of the proposed methodology.     

Both Tough and Soft Double Network Hydrogel Nanocomposite Based on O-Carboxymethyl Chitosan/Poly(vinyl alcohol) and Graphene Oxide: A Promising Alternative for Tissue Engineering

A reinforced double network (DN) hydrogel as a candidate for skin scaffold was prepared. It consists of O-carboxymethyl chitosan, polyvinyl alcohol, honey, CaCl₂, and graphene oxide. The various concentrations of CaCl₂, namely, 30, 45, and 60 wt% were investigated. Besides, the GO content was studied as 3, 5, and 10 wt%. The structure of the DN was characterized by Fourier-transform infrared spectroscopy, thermogravimetric analysis, differential scanning calorimetry, X-ray diffraction, energy dispersive X-ray and Brunauer-Emmett-Teller were evaluated. The mechanical properties were studied, too. It showed that the DN with 45 wt% CaCl₂ was optimized. Also, swelling mechanism was investigated. Moreover, with the increase in GO content, the mechanical strength was increased. The cytotoxicity was evaluated and it was indicated that DN with 45% CaCl₂ and 5% GO had an acceptable level of biocompatibility. Moreover, scanning electron microscopy images showed the porous structure of the DN nanocomposite. Thus, the DN has the potential to be applied in tissue engineering as a skin scaffold. POLYM. ENG. SCI., 60:889–899, 2020. © 2020 Society of Plastics Engineers     

Techniques,perspectives, and challenges of bioactive peptide generation: A comprehensive systematic review

Due to the digestible refractory and absorbable structures of bioactive peptides (BPs), they could induce notable biological impacts on the living organism. In this regard, the current study was devoted to providing an overview regarding the available methods for BPs generation by the aid of a systematic review conducted on the published articles up to April 2019. In this context, the PubMed and Scopus databases were screened to retrieve the related publications. According to the results, although the characterization of BPs mainly has been performed using enzymatic and microbial in‐vitro methods, they cannot be considered as suitable techniques for further stimulation of digestion in the gastrointestinal tract. Therefore, new approaches for both in‐vivo and in‐silico methods for BPs identification should be developed to overcome the obstacles that belonged to the current methods. The purpose of this review was to compile the recent analytical methods applied for studying various aspects of food‐derived biopeptides, and emphasizing generation at in vitro, in vivo, and in silico.